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Vassil Momtchev

CTO, Ontotext
Vassil has more than 15 years in software development in various domains like life sciences, pharmaceutical, health care and telecommunication. In the past 10 years he mostly engaged with the development of complex enterprise knowledge management solutions that features natural language processing, text analytics, reasoning, semantics, ontology design, linked data, conceptual model design, implementation of formal grammars and graph databases. During Vassil's career he was involved in small startups that developed in bigger organization and also in a large global organization.

Matching Videos

4 Matching Videos

May 6th 2020, 11:20pm EST

Vassil Momtchev announces new features of GraphDB and Ontotext platform

May 07th 2020, 11:20am EST

Francois Scharffe leads the Q&A between Vassil Momtchev from Ontotext and John Sowa from Kyndi.

May 5th 2020, 1:30pm EST

The enterprise knowledge graphs help modern organizations to preserve the semantic context of abundant accessible information. They become the backbone of enterprise knowledge management and AI technologies with the ability to differentiate things versus strings. Still, beyond the hype of repackaging the semantic web standards for enterprise, few practical tutorials are demonstrating how to build and maintain an enterprise knowledge graph. This tutorial helps you learn how to build an enterprise knowledge graph beyond the RDF database and SPARQL with GraphQL protocol. Overcome critical challenges like exposing simple to use interface for data consumption to users who may be unfamiliar with information schemas. Control information access by implementing robust security. Open the graph for updates, but preserve its consistency and quality. You will pass step by step process to (1) start a knowledge graph from a public RDF dataset, (2) generate GraphQL API to abstract the RDF database, (3) pass a quick GraphQL crash course with examples (4) develop a sample web application. Finally, we will discuss other possible directions like extending the knowledge graph with machine learning components, extend the graph with additional services, add monitoring dashboards, integrate external systems. The tutorial is based on Ontotext GraphDB and Platform products and requires basic RDF and SPARQL knowledge.

May 7th 2020, 11:20am EST

Semantic Objects (SOML) are used to generate GraphQL schema and RDF shapes and to determine access control without application server/API development efforts. We can also generate SOML from OWL ontologies and RDFS schema. The result is a platform that combines the comprehension of SPARQL engine with the scalability of MongoDB and search capabilities of Elastic and provides GraphQL interfaces that allow federation of 3rd party (non-semantic) sources.